Handbook of Measurement Error Models (Chapman & Hall/CRC Handbooks of Modern Statistical Methods)

Measurement error arises ubiquitously in applications and has been of long-standing concern in a variety of fields, including medical research, epidemiological studies, economics, environmental studies, and survey research. While several research monographs are available to summarize methods and strategies of handling different measurement error problems, research in this area continues to attract extensive attention. The Handbook of Measurement Error Models provides overviews of various topics on measurement error problems. It collects carefully edited chapters concerning issues of measurement error and evolving statistical methods, with a good balance of methodology and applications. It is prepared for readers who wish to start research and gain insights into challenges, methods, and applications related to error-prone data. It also serves as a reference text on statistical methods and applications pertinent to measurement error models, for researchers and data analysts alike. Features: Provides an account of past development and modern advancement concerning measurement error problems Highlights the challenges induced by error-contaminated data Introduces off-the-shelf methods for mitigating deleterious impacts of measurement error Describes state-of-the-art strategies for conducting in-depth research "Measurement error arises ubiquitously in applications and has been of long-standing concern in a variety of fields, including medical research, epidemiological studies, economics, environmental studies, and survey research. While several research monographs are available to summarize methods and strategies of handling different measurement error problems, research in this area continues to attract extensive attention. The Handbook of Measurement Error provides overviews of various topics on measurement error problems. It collects carefully edited chapters concerning issues of measurement error and evolving statistical methods, with a good balance of methodology and applications. It is prepared for readers who wish to start research and gain insights into challenges, methods, and applications related to error-prone data. It also serves as a reference text on statistical methods and applications pertinent to measurement error models, for researchers and data analysts alike. Features: Provides an account of past development and modern advancement concerning measurement error problems; Highlights the challenges induced by error-contaminated data; Introduces off-the-shelf methods for mitigating deleterious impacts of measurement error; Describes state-of-the-art strategies for conducting in-depth research. Grace Y. Yi is Professor of Statistics at the University of Western Ontario where she holds a Tier I Canada Research Chair in Data Science. She is a Fellow of the Institute of Mathematical Statistics (IMS), a Fellow of the American Statistical Association (ASA), and an Elected Member of the International Statistical Institute (ISI). She authored the monograph Statistical Analysis with Measurement Error or Misclassification (2017, Springer). Aurore Delaigle is Professor at the School of Mathematics and Statistics at the University of Melbourne. She is a Fellow of the Australian Academy of Science, a Fellow of the Institute of Mathematical Statistics (IMS), a Fellow of the American Statistical Association (ASA), and an Elected Member of the International Statistical Institute (ISI). She is a past recipient of the George W. Snedecor Award from the Committee of Presidents of Statistical Societies (COPSS) and of the Moran Medal from the Australian Academy of Science. Paul Gustafson is Professor and Head of the Department of Statistics at the University of British Columbia. He is a Fellow of the American Statistical Association, the 2020 Gold Medalist of the Statistical Society of Canada, and the author of the monograph Measurement Error and Misclassification in Statistics and Epidemiology: Impacts and Bayesian Adjustments (2004, Chapman and Hall, CRC Press)"-- Provided by publisher. Measurement error models--a brief account of past developments and modern advancements / Grace Y. Yi/Jeffrey S Buzas -- The impact of unacknowledged measurement error / Paul Gustafson -- Identifiability in measurement error / Liqun Wang -- Partial learning of misclassification parameters / Paul Gustafson -- Using instrumental variables to estimate models with mismeasured regressors / Arthur Lewbel -- Likelihood methods for measurement error and misclassification / Grace Y. Yi -- Regression calibration for covariate measurement error / Pamela A. Shaw -- Conditional and corrected score methods / David M. Zucker -- Semiparametric methods for measurement error and misclassification / Yanyuan Ma -- Deconvolution kernel density estimation / Aurore Delaigle -- Nonparametric deconvolution by Fourier transformation and other related approaches / Yicheng Kang/Peihua Qiu -- Deconvolution with unknown error distribution / Aurore Delaigle, Ingrid Van Keilegom -- Nonparametric inference methods for Berkson errors / Weixing Song -- Nonparametric measurement errors models for regression / Tatiyana Apanasovich/Hua Liang -- Covariate measurement error in survival data / Jeffrey S. Buzas -- Mixed effects models with measurement errors in time-dependent covariates / Lang Wu/Wei Liu/Hongbin Zhang -- Estimation in mixed-effects models with measurement error -- Liqun Wang -- Measurement error in dynamic models -- John P. Buonaccorsi -- Spatial exposure measurement error in environmental epidemiology -- Howard H. Chang, Joshua P. Keller -- Measurement error as a missing data problem -- Ruth H. Keogh, Jonathan W. Bartlett -- Measurement error in causal inference -- Linda Valeri -- Measurement error and misclassification in meta-analysis -- Annamaria Guolo -- Bayesian adjustment for misclassification -- James D. Stamey and John W. Seaman Jr. -- Bayesian approaches for handling covariate measurement error -- Samiran Sinha Cover Half Title Series Page Title Page Copyright Page Contents Preface Editors Contributors Part I: Introduction 1. Measurement Error Models - A Brief Account of Past Developments and Modern Advancements 2. The Impact of Unacknowledged Measurement Error Part II: Identifiability and Estimation 3. Identifiability in Measurement Error Models 4. Partial Learning of Misclassification Parameters 5. Using Instrumental Variables to Estimate Models with Mismeasured Regressors Part III: General Methodology 6. Likelihood Methods with Measurement Error and Misclassification 7. Regression Calibration for Covariate Measurement Error 8. Conditional and Corrected Score Methods 9. Semiparametric Methods for Measurement Error and Misclassification Part IV: Nonparametric Inference 10. Deconvolution Kernel Density Estimation 11. Nonparametric Deconvolution by Fourier Transformation and Other Related Approaches 12. Deconvolution with Unknown Error Distribution 13. Nonparametric Inference Methods for Berkson Errors 14. Nonparametric Measurement Errors Models for Regression Part V: Applications 15. Covariate Measurement Error in Survival Data 16. Mixed Effects Models with Measurement Errors in Time-Dependent Covariates 17. Estimation in Mixed-effects Models with Measurement Error 18. Measurement Error in Dynamic Models 19. Spatial Exposure Measurement Error in Environmental Epidemiology Part VI: Other Features 20. Measurement Error as a Missing Data Problem 21. Measurement Error in Causal Inference 22. Measurement Error and Misclassification in Meta-Analysis Part VII: Bayesian Analysis 23. Bayesian Adjustment for Misclassification 24. Bayesian Approaches for Handling Covariate Measurement Error Author Index Subject Index